Details

Autor(en) / Beteiligte

• Lika, Blerina
• Kolomvatsos, Kostas
• Hadjiefthymiades, Stathes

Titel

Facing the cold start problem in recommender systems

Ist Teil von

• Expert systems with applications, 2014-03, Vol.41 (4), p.2065-2073

Ort / Verlag

Amsterdam: Elsevier Ltd

Erscheinungsjahr

2014

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •We propose a RS that combines classification, semantic and heuristic methods.•We propose the use of known classification algorithms for creating groups of users.•Semantic similarity techniques result the similarity between users.•We propose a weighted scheme for resulting the final prediction of ratings. A recommender system (RS) aims to provide personalized recommendations to users for specific items (e.g., music, books). Popular techniques involve content-based (CB) models and collaborative filtering (CF) approaches. In this paper, we deal with a very important problem in RSs: The cold start problem. This problem is related to recommendations for novel users or new items. In case of new users, the system does not have information about their preferences in order to make recommendations. We propose a model where widely known classification algorithms in combination with similarity techniques and prediction mechanisms provide the necessary means for retrieving recommendations. The proposed approach incorporates classification methods in a pure CF system while the use of demographic data help for the identification of other users with similar behavior. Our experiments show the performance of the proposed system through a large number of experiments. We adopt the widely known dataset provided by the GroupLens research group. We reveal the advantages of the proposed solution by providing satisfactory numerical results in different experimental scenarios.